Higgsfield
Higgsfield is an open-source, fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters, such as Large Language Models (LLMs). 1. Allocating exclusive and non-exclusive access to compute resources (nodes) to users for their training tasks. 2. Supporting ZeRO-3 deepspeed API and fully sharded data parallel API of PyTorch, enabling efficient sharding for trillion-parameter models. 3. Offering a framework for initiating, executing, and monitoring the training of large neural networks on allocated nodes. 4. Managing resource contention by maintaining a queue for running experiments. 5. Facilitating continuous integration of machine learning development through seamless integration with GitHub and GitHub Actions. Higgsfield streamlines the process of training massive models and empowers developers with a versatile and robust toolset. 1. We install all the required tools in your server (Docker, your project's deploy keys, higgsfield binary). 2. Then we generate deploy & run workflows for your experiments. 3. As soon as it gets into Github, it will automatically deploy your code on your nodes. 4. Then you access your experiments' run UI through Github, which will launch experiments and save the checkpoints.
View Higgsfield on GitHub
Higgsfield is an open-source, fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters, such as Large Language Models (LLMs).
- Allocating exclusive and non-exclusive access to compute resources (nodes) to users for their training tasks. 2. Supporting ZeRO-3 deepspeed API and fully sharded data parallel API of PyTorch, enabling efficient sharding for trillion-parameter models. 3. Offering a framework for initiating, executing, and monitoring the training of large neural networks on allocated nodes. 4. Managing resource contention by maintaining a queue for running experiments. 5. Facilitating continuous integration of machine learning development through seamless integration with GitHub and GitHub Actions. Higgsfield streamlines the process of training massive models and empowers developers with a versatile and robust toolset.
- We install all the required tools in your server (Docker, your project's deploy keys, higgsfield binary). 2. Then we generate deploy & run workflows for your experiments. 3. As soon as it gets into Github, it will automatically deploy your code on your nodes. 4. Then you access your experiments' run UI through Github, which will launch experiments and save the checkpoints.
Higgsfield at a glance
| Stars | 4.6k |
|---|---|
| Forks | 858 |
| Language | Jupyter Notebook |
| License | Apache-2.0 |
| Last update | 2026-09-14 |
| Contributors | 4 |
How to install Higgsfield
bash pip install higgsfield==0.0.3
Where Higgsfield is listed
Navid Moazzez
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Navid Moazzez helps creators and founders master AI and build their own AI Operating System (AI OS) to automate their business and life.
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